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Record W2325665003 · doi:10.1021/jp1067439

Examination of Water Electrolysis and Oxygen Reduction As Self-Discharge Mechanisms for Carbon-Based, Aqueous Electrolyte Electrochemical Capacitors

2011· article· en· W2325665003 on OpenAlexaff
Alicia M. Oickle, Heather A. Andreas

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2011
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolysisElectrolyteElectrolysis of waterSelf-dischargeElectrochemistryChemistryPolymer electrolyte membrane electrolysisHydrogenOxygenElectrodeInorganic chemistryCarbon fibersHigh-pressure electrolysisAqueous solutionReversible hydrogen electrodeFaraday efficiencyChemical engineeringMaterials scienceWorking electrodeComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Water electrolysis and oxygen reduction as possible self-discharge mechanisms for carbon-based, aqueous H 2 SO 4 electrolyte electrochemical capacitors is examined through a comparison of the predicted and actual effects of varying the dissolved oxygen and hydrogen content on self-discharge. Water electrolysis, in the form of oxygen evolution, is not the self-discharge mechanism on the positive electrode, although, the self-discharge profile is consistent with an activation-controlled Faradaic discharge or charge redistribution mechanism. The addition of hydrogen evidences no change in self-discharge from the negative electrode, and the profile is consistent with a diffusion-controlled mechanism, suggesting water electrolysis through hydrogen evolution is not the self-discharge mechanism. Oxygen reduction causes a large increase in self-discharge on the negative electrode which necessitates purging the cell of oxygen. As such, water electrolysis is likely not the cause of self-discharge in carbon-based, aqueous electrolyte electrochemical capacitors but oxygen reduction is a cause of increased self-discharge on the negative electrode.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.202
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations67
Published2011
Admission routes1
Has abstractyes

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